ArticleBMC medical research methodology2021
Inferencing superspreading potential using zero-truncated negative binomial model: exemplification with COVID-19.
Article in BMC medical research methodology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.
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Who cites it
19 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Superspreading, overdispersion and their implications in the SARS-CoV-2 (COVID-19) pandemic: a systematic review and meta-analysis of the literature.BMC public health · 2023Pooled it
- Systematic review and meta-analyses of superspreading of SARS-CoV-2 infections.Transboundary and emerging diseases · 2022Pooled it
- Robustness of zero truncated negative binomial models over traditional models with application to analysing factor association to length of ICU stay in a middle-income country.BMC research notes · 2026Article
- Estimating Re and overdispersion in secondary cases from the size of identical sequence clusters of SARS-CoV-2.PLoS computational biology · 2025Article
- Forecasting the Monkeypox Outbreak Using Limited Data: A Case Study of Thailand.Interdisciplinary perspectives on infectious diseases · 2025Article
- Systems Thinking, Causal Loop Diagram, and Systems Dynamic in Public Health Challenges: Navigating Long COVID Syndrome and Sense of Smell in LGBTQIA+ Communities.Public health challenges · 2024Review
- Time series analysis and short-term forecasting of monkeypox outbreak trends in the 10 major affected countries.BMC infectious diseases · 2024Article
- The effect of variation of individual infectiousness on SARS-CoV-2 transmission in households.eLife · 2023Article
- On Spatiotemporal Overdispersion and Macroparasite Accumulation in Hosts Leading to Aggregation: A Quantitative Framework.Diseases (Basel, Switzerland) · 2022Article
- The role of inter-regional mobility in forecasting SARS-CoV-2 transmission.Journal of the Royal Society, Interface · 2022Article
- Characterizing superspreading potential of infectious disease: Decomposition of individual transmissibility.PLoS computational biology · 2022Article
- Modelling COVID-19 outbreak on the Diamond Princess ship using the public surveillance data.Infectious Disease Modelling · 2022Article
- Comparisons of statistical distributions for cluster sizes in a developing pandemic.BMC medical research methodology · 2022Article
- System dynamics analysis of COVID-19 prevention and control strategies.Environmental science and pollution research international · 2022Article
- Shrinkage in serial intervals across transmission generations of COVID-19.Journal of theoretical biology · 2021Article
- Characterization of Unlinked Cases of COVID-19 and Implications for Contact Tracing Measures: Retrospective Analysis of Surveillance Data.JMIR public health and surveillance · 2021Article
- A particle swarm optimization approach for predicting the number of COVID-19 deaths.Scientific reports · 2021Review
- The co-circulating transmission dynamics of SARS-CoV-2 Alpha and Eta variants in Nigeria: A retrospective modeling study of COVID-19.Journal of global health · 2021Article
- Superspreading and heterogeneity in transmission of SARS, MERS, and COVID-19: A systematic review.Computational and structural biotechnology journal · 2021Review
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Authors and funding
10 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundIn infectious disease transmission dynamics, the high heterogeneity in individual infectiousness indicates that few index cases generate large numbers of secondary cases, which is commonly known as superspreading events. The heterogeneity in transmission can be measured by describing the distribution of the number of secondary cases as a negative binomial (NB) distribution with dispersion parameter, k. However, such inference framework usually neglects the under-ascertainment of sporadic cases, which are those without known epidemiological link and considered as independent clusters of size one, and this may potentially bias the estimates.
methodsIn this study, we adopt a zero-truncated likelihood-based framework to estimate k. We evaluate the estimation performance by using stochastic simulations, and compare it with the baseline non-truncated version. We exemplify the analytical framework with three contact tracing datasets of COVID-19.
resultsWe demonstrate that the estimation bias exists when the under-ascertainment of index cases with 0 secondary case occurs, and the zero-truncated inference overcomes this problem and yields a less biased estimator of k. We find that the k of COVID-19 is inferred at 0.32 (95%CI: 0.15, 0.64), which appears slightly smaller than many previous estimates. We provide the simulation codes applying the inference framework in this study.
conclusionsThe zero-truncated framework is recommended for less biased transmission heterogeneity estimates. These findings highlight the importance of individual-specific case management strategies to mitigate COVID-19 pandemic by lowering the transmission risks of potential super-spreaders with priority.
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